OpenRaiser/Intern-Atlas huggingface dataset:
Run-Time-Reconfigurable Multi-Precision Floating-Point Matrix Multiplier Intellectual Property Core on FPGA
Venue: arXiv cs.AR 2019
Year: 2019

In todays world, high-power computing applications such as image processing, digital signal processing, graphics, and robotics require enormous computing power. These applications use matrix operations, especially matrix multiplication. Multiplication operations require a lot of computational time and are also complex in design. We can use field-programmable gate arrays as low-cost hardware accelerators along with a low-cost general-purpose processor instead of a high-cost application-specific processor for such applications. In this work, we employ an efficient Strassens algorithm for matrix multiplication and a highly efficient run-time-reconfigurable floating-point multiplier for matrix element multiplication. The run-time-reconfigurable floating-point multiplier is implemented with custom floating-point format for variable-precision applications. A very efficient combination of Karatsuba algorithm and Urdhva Tiryagbhyam algorithm is used to implement the binary multiplier. This design can effectively adjust the power and delay requirements according to different accuracy requirements by reconfiguring itself during run time.
Methods and open-source toolkit for analyzing and visualizing challenge results
Venue: arXiv cs.CV 2019
Year: 2019

Biomedical challenges have become the de facto standard for benchmarking biomedical image analysis algorithms. While the number of challenges is steadily increasing, surprisingly little effort has been invested in ensuring high quality design, execution and reporting for these international competitions. Specifically, results analysis and visualization in the event of uncertainties have been given almost no attention in the literature. Given these shortcomings, the contribution of this paper is two-fold: (1) We present a set of methods to comprehensively analyze and visualize the results of single-task and multi-task challenges and apply them to a number of simulated and real-life challenges to demonstrate their specific strengths and weaknesses; (2) We release the open-source framework challengeR as part of this work to enable fast and wide adoption of the methodology proposed in this paper. Our approach offers an intuitive way to gain important insights into the relative and absolute performance of algorithms, which cannot be revealed by commonly applied visualization techniques. This is demonstrated by the experiments performed within this work. Our framework could thus become an important tool for analyzing and visualizing challenge results in the field of biomedical image analysis and beyond.
Automating dynamic consent decisions for the processing of social media data in health research
Venue: arXiv cs.HC 2019
Year: 2019

Social media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even obtained, are typically broad and inflexible, or place a significant burden on the participant. Machine learning algorithms show much promise for facilitating a 'middle ground' approach: using trained models to predict and automate granular consent decisions. Such techniques, however, raise a myriad of follow-on ethical and technical considerations. In this paper, we present an exploratory user study (n = 67) in which we find that we can predict the appropriate flow of health-related social media data with reasonable accuracy, while minimising undesired data leaks. We then attempt to deconstruct the findings of this study, identifying and discussing a number of real-world implications if such a technique were put into practice.
The Emergence of Compositional Languages for Numeric Concepts Through Iterated Learning in Neural Agents
Venue: arXiv cs.CL 2019
Year: 2019

Since first introduced, computer simulation has been an increasingly important tool in evolutionary linguistics. Recently, with the development of deep learning techniques, research in grounded language learning has also started to focus on facilitating the emergence of compositional languages without pre-defined elementary linguistic knowledge. In this work, we explore the emergence of compositional languages for numeric concepts in multi-agent communication systems. We demonstrate that compositional language for encoding numeric concepts can emerge through iterated learning in populations of deep neural network agents. However, language properties greatly depend on the input representations given to agents. We found that compositional languages only emerge if they require less iterations to be fully learnt than other non-degenerate languages for agents on a given input representation.
EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM
Venue: arXiv cs.DC 2019
Year: 2019

The effectiveness of deep neural networks (DNN) in vision, speech, and language processing has prompted a tremendous demand for energy-efficient high-performance DNN inference systems. Due to the increasing memory intensity of most DNN workloads, main memory can dominate the system's energy consumption and stall time. One effective way to reduce the energy consumption and increase the performance of DNN inference systems is by using approximate memory, which operates with reduced supply voltage and reduced access latency parameters that violate standard specifications. Using approximate memory reduces reliability, leading to higher bit error rates. Fortunately, neural networks have an intrinsic capacity to tolerate increased bit errors. This can enable energy-efficient and high-performance neural network inference using approximate DRAM devices. Based on this observation, we propose EDEN, a general framework that reduces DNN energy consumption and DNN evaluation latency by using approximate DRAM devices, while strictly meeting a user-specified target DNN accuracy. EDEN relies on two key ideas: 1) retraining the DNN for a target approximate DRAM device to increase the DNN's error tolerance, and 2) efficient mapping of the error tolerance of each individual DNN data type to a corresponding approximate DRAM partition in a way that meets the user-specified DNN accuracy requirements. We evaluate EDEN on multi-core CPUs, GPUs, and DNN accelerators with error models obtained from real approximate DRAM devices. For a target accuracy within 1% of the original DNN, our results show that EDEN enables 1) an average DRAM energy reduction of 21%, 37%, 31%, and 32% in CPU, GPU, and two DNN accelerator architectures, respectively, across a variety of DNNs, and 2) an average (maximum) speedup of 8% (17%) and 2.7% (5.5%) in CPU and GPU architectures, respectively, when evaluating latency-bound DNNs.
AffWild Net and Aff-Wild Database
Venue: arXiv cs.LG 2019
Year: 2019

Emotions recognition is the task of recognizing people's emotions. Usually it is achieved by analyzing expression of peoples faces. There are two ways for representing emotions: The categorical approach and the dimensional approach by using valence and arousal values. Valence shows how negative or positive an emotion is and arousal shows how much it is activated. Recent deep learning models, that have to do with emotions recognition, are using the second approach, valence and arousal. Moreover, a more interesting concept, which is useful in real life is the "in the wild" emotions recognition. "In the wild" means that the images analyzed for the recognition task, come from from real life sources(online videos, online photos, etc.) and not from staged experiments. So, they introduce unpredictable situations in the images, that have to be modeled. The purpose of this project is to study the previous work that was done for the "in the wild" emotions recognition concept, design a new dataset which has as a standard the "Aff-wild" database, implement new deep learning models and evaluate the results. First, already existing databases and deep learning models are presented. Then, inspired by them a new database is created which includes 507.208 frames in total from 106 videos, which were gathered from online sources. Then, the data are tested in a CNN model based on CNN-M architecture, in order to be sure about their usability. Next, the main model of this project is implemented. That is a Regression GAN which can execute unsupervised and supervised learning at the same time. More specifically, it keeps the main functionality of GANs, which is to produce fake images that look as good as the real ones, while it can also predict valence and arousal values for both real and fake images. Finally, the database created earlier is applied to this model and the results are presented and evaluated.
Illegible Text to Readable Text: An Image-to-Image Transformation using Conditional Sliced Wasserstein Adversarial Networks
Venue: arXiv cs.CV 2019
Year: 2019

Automatic text recognition from ancient handwritten record images is an important problem in the genealogy domain. However, critical challenges such as varying noise conditions, vanishing texts, and variations in handwriting make the recognition task difficult. We tackle this problem by developing a handwritten-to-machine-print conditional Generative Adversarial network (HW2MP-GAN) model that formulates handwritten recognition as a text-Image-to-text-Image translation problem where a given image, typically in an illegible form, is converted into another image, close to its machine-print form. The proposed model consists of three-components including a generator, and word-level and character-level discriminators. The model incorporates Sliced Wasserstein distance (SWD) and U-Net architectures in HW2MP-GAN for better quality image-to-image transformation. Our experiments reveal that HW2MP-GAN outperforms state-of-the-art baseline cGAN models by almost 30 in Frechet Handwritten Distance (FHD), 0.6 on average Levenshtein distance and 39% in word accuracy for image-to-image translation on IAM database. Further, HW2MP-GAN improves handwritten recognition word accuracy by 1.3% compared to baseline handwritten recognition models on the IAM database.
Analytical Performance Evaluation of Beamforming Under Transceivers Hardware Imperfections
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we provide the mathematical framework to evaluate and quantify the performance of wireless systems, which employ beamforming, in the presence of hardware imperfections at both the basestation and the user equipment. In more detail, by taking a macroscopic view of the joint impact of hardware imperfections, we introduce a general model that accounts for transceiver impairments in beamforming transmissions. In order to evaluate their impact, we present novel closed form expressions for the outage probability and upper bounds for the characterization of the system's capacity. Our analysis reveals that the level of imperfection can significantly constraint the ergodic capacity. Therefore, it is important to take them into account when evaluating and designing beamforming systems.
ClassyTune: A Performance Auto-Tuner for Systems in the Cloud
Venue: arXiv cs.PF 2019
Year: 2019

Performance tuning can improve the system performance and thus enable the reduction of cloud computing resources needed to support an application. Due to the ever increasing number of parameters and complexity of systems, there is a necessity to automate performance tuning for the complicated systems in the cloud. The state-of-the-art tuning methods are adopting either the experience-driven tuning approach or the data-driven one. Data-driven tuning is attracting increasing attentions, as it has wider applicability. But existing data-driven methods cannot fully address the challenges of sample scarcity and high dimensionality simultaneously. We present ClassyTune, a data-driven automatic configuration tuning tool for cloud systems. ClassyTune exploits the machine learning model of classification for auto-tuning. This exploitation enables the induction of more training samples without increasing the input dimension. Experiments on seven popular systems in the cloud show that ClassyTune can effectively tune system performance to seven times higher for high-dimensional configuration space, outperforming expert tuning and the state-of-the-art auto-tuning solutions. We also describe a use case in which performance tuning enables the reduction of 33% computing resources needed to run an online stateless service.
Generative Neural Network based Spectrum Sharing using Linear Sum Assignment Problems
Venue: arXiv cs.LG 2019
Year: 2019

Spectrum management and resource allocation (RA) problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks. The traditional approaches for solving such problems usually consume time and memory, especially for large size problems. Recently different machine learning approaches have been considered as potential promising techniques for combinatorial optimization problems, especially the generative model of the deep neural networks. In this work, we propose a resource allocation deep autoencoder network, as one of the promising generative models, for enabling spectrum sharing in underlay device-to-device (D2D) communication by solving linear sum assignment problems (LSAPs). Specifically, we investigate the performance of three different architectures for the conditional variational autoencoders (CVAE). The three proposed architecture are the convolutional neural network (CVAE-CNN) autoencoder, the feed-forward neural network (CVAE-FNN) autoencoder, and the hybrid (H-CVAE) autoencoder. The simulation results show that the proposed approach could be used as a replacement of the conventional RA techniques, such as the Hungarian algorithm, due to its ability to find solutions of LASPs of different sizes with high accuracy and very fast execution time. Moreover, the simulation results reveal that the accuracy of the proposed hybrid autoencoder architecture outperforms the other proposed architectures and the state-of-the-art DNN techniques.
Template-Instance Loss for Offline Handwritten Chinese Character Recognition
Venue: arXiv cs.CV 2019
Year: 2019

The long-standing challenges for offline handwritten Chinese character recognition (HCCR) are twofold: Chinese characters can be very diverse and complicated while similarly looking, and cursive handwriting (due to increased writing speed and infrequent pen lifting) makes strokes and even characters connected together in a flowing manner. In this paper, we propose the template and instance loss functions for the relevant machine learning tasks in offline handwritten Chinese character recognition. First, the character template is designed to deal with the intrinsic similarities among Chinese characters. Second, the instance loss can reduce category variance according to classification difficulty, giving a large penalty to the outlier instance of handwritten Chinese character. Trained with the new loss functions using our deep network architecture HCCR14Layer model consisting of simple layers, our extensive experiments show that it yields state-of-the-art performance and beyond for offline HCCR.
Minimal Assumptions Refinement for GR(1) Specifications
Venue: arXiv cs.LO 2019
Year: 2019

Reactive synthesis is concerned with finding a correct-by-construction controller from formal specifications, typically expressed in Linear Temporal Logic (LTL). The specifications describe assumptions about an environment and guarantees to be achieved by the controller operating in that environment. If a controller exists, given the assumptions, the specification is said to be realizable. This paper focuses on finding a minimal set of assumptions that guarantee realizability in the context of counterstrategy-guided assumption refinement procedures. Specifically, we introduce the notion of minimal assumptions refinements and provide an algorithm that provably computes these with little time overhead. We show experimentally, using common benchmarks, that embedding our algorithm in state-of-the-art approaches for assumption refinement results in consistently shorter solutions than without such embedding, and allows to explore a higher number of candidate solutions. We also propose a hybrid variant for dealing with the higher sparsity of solutions in the space of minimal refinements and show that its application speeds up the identification of a solution.
Geomancer: An Open-Source Framework for Geospatial Feature Engineering
Venue: arXiv cs.LG 2019
Year: 2019

This paper presents Geomancer, an open-source framework for geospatial feature engineering. It simplifies the acquisition of geospatial attributes for downstream, large-scale machine learning tasks. Geomancer leverages any geospatial dataset stored in a data warehouse, users need only to define the features (Spells) they want to create, and cast them on any spatial dataset. In addition, these features can be exported into a JSON file (SpellBook) for sharing and reproducibility. Geomancer has been useful to some of our production use-cases such as property value estimation, area valuation, and more. It is available on Github, and can be installed from PyPI.
Improved (In-)Approximability Bounds for d-Scattered Set
Venue: arXiv cs.CC 2019
Year: 2019

In the $d$-Scattered Set problem we are asked to select at least $k$ vertices of a given graph, so that the distance between any pair is at least $d$. We study the problem's (in-)approximability and offer improvements and extensions of known results for Independent Set, of which the problem is a generalization. Specifically, we show: - A lower bound of $Δ^{\lfloor d/2\rfloor-ε}$ on the approximation ratio of any polynomial-time algorithm for graphs of maximum degree $Δ$ and an improved upper bound of $O(Δ^{\lfloor d/2\rfloor})$ on the approximation ratio of any greedy scheme for this problem. - A polynomial-time $2\sqrt{n}$-approximation for bipartite graphs and even values of $d$, that matches the known lower bound by considering the only remaining case. - A lower bound on the complexity of any $ρ$-approximation algorithm of (roughly) $2^{\frac{n^{1-ε}}{ρd}}$ for even $d$ and $2^{\frac{n^{1-ε}}{ρ(d+ρ)}}$ for odd $d$ (under the randomized ETH), complemented by $ρ$-approximation algorithms of running times that (almost) match these bounds.
SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text
Venue: arXiv cs.CL 2019
Year: 2019

Contemporary datasets on tobacco consumption focus on one of two topics, either public health mentions and disease surveillance, or sentiment analysis on topical tobacco products and services. However, two primary considerations are not accounted for, the language of the demographic affected and a combination of the topics mentioned above in a fine-grained classification mechanism. In this paper, we create a dataset of 3144 tweets, which are selected based on the presence of colloquial slang related to smoking and analyze it based on the semantics of the tweet. Each class is created and annotated based on the content of the tweets such that further hierarchical methods can be easily applied. Further, we prove the efficacy of standard text classification methods on this dataset, by designing experiments which do both binary as well as multi-class classification. Our experiments tackle the identification of either a specific topic (such as tobacco product promotion), a general mention (cigarettes and related products) or a more fine-grained classification. This methodology paves the way for further analysis, such as understanding sentiment or style, which makes this dataset a vital contribution to both disease surveillance and tobacco use research.
Trajectory optimization for a class of robots belonging to Constrained Collaborative Mobile Agents (CCMA) family
Venue: arXiv cs.RO 2019
Year: 2019

We present a novel class of robots belonging to Constrained Collaborative Mobile Agents (CCMA) family which consists of ground mobile bases with non-holonomic constraints. Moreover, these mobile robots are constrained by closed-loop kinematic chains consisting of revolute joints which can be either passive or actuated. We also describe a novel trajectory optimization method which is general with respect to number of mobile robots, topology of the closed-loop kinematic chains and placement of the actuators at the revolute joints. We also extend the standalone trajectory optimization method to optimize concurrently the design parameters and the control policy. We describe various CCMA system examples, in simulation, differing in design, topology, number of mobile robots and actuation space. The simulation results for standalone trajectory optimization with fixed design parameters is presented for CCMA system examples. We also show how this method can be used for tasks other than end-effector positioning such as internal collision avoidance and external obstacle avoidance. The concurrent design and control policy optimization is demonstrated, in simulations, to increase the CCMA system workspace and manipulation capabilities. Finally, the trajectory optimization method is validated in experiments through two 4-DOF prototypes consisting of 3 tracked mobile bases.
IoT or NoT: Identifying IoT Devices in a ShortTime Scale
Venue: arXiv cs.NI 2019
Year: 2019

In recent years the number of IoT devices in home networks has increased dramatically. Whenever a new device connects to the network, it must be quickly managed and secured using the relevant security mechanism or QoS policy. Thus a key challenge is to distinguish between IoT and NoT devices in a matter of minutes. Unfortunately, there is no clear indication of whether a device in a network is an IoT. In this paper, we propose different classifiers that identify a device as IoT or non-IoT, in a short time scale, and with high accuracy. Our classifiers were constructed using machine learning techniques on a seen (training) dataset and were tested on an unseen (test) dataset. They successfully classified devices that were not in the seen dataset with accuracy above 95%. The first classifier is a logistic regression classifier based on traffic features. The second classifier is based on features we retrieve from DHCP packets. Finally, we present a unified classifier that leverages the advantages of the other two classifiers. We focus on the home-network environment, but our classifiers are also applicable to enterprise networks.
Analyzing User Activities Using Vector Space Model in Online Social Networks
Venue: arXiv cs.LG 2019
Year: 2019

The increasing popularity of internet, wireless technologies and mobile devices has led to the birth of mass connectivity and online interaction through Online Social Networks (OSNs) and similar environments. OSN reflects a social structure consist of a set of individuals and different types of ties like connections, relationships, interactions etc among them and helps its users to connect with their friends and common interest groups, share views and to pass information. Now days the users choose OSN sites as a most preferred place for sharing their updates, different views, posting photographs and would like to make it available for others for viewing, rating and making comments. The current paper aims to explore and analyze the association between the objects (like photographs, posts etc) and its viewers (friends, acquaintances etc) for a given user and to find activity relationship among them by using the TF-IDF scheme of Vector Space Model. After vectorization the vector data has been presented through a weighted graph with various properties.